dogs vs. cats project

项目背景

---

本项目是Kaggle上的一个已经结束的比赛项目。

• 数据源：kaggle dogs-vs-cats项目公共数据集 下载地址：https://www.kaggle.com/c/dogs-vs-cats-redux-kernels-edition/data
• 目标：使用三个模型：Xception，InceptionResNetV2，DenseNet201进行融合，对融合后的模型fine-tune后对test数据集进行预测，并达到项目规定的要求.
• 要求：kaggle Public Leaderboard 前10%，根据https://www.kaggle.com/c/dogs-vs-cats-redux-kernels-edition/leaderboard ，10%位置的score大约为0.06149.

# 下载项目公共数据集
!wget -c "https://storage.googleapis.com/kaggle-competitions-data/kaggle/5441/train.zip?GoogleAccessId=web-data@kaggle-161607.iam.gserviceaccount.com&Expires=1529407270&Signature=TTmj2gZCjWT%2FjzsB1hBLfYURi6aN6LKWNDnKYbW4efA8WYi3D2fvUuO0tGIPtGh5SjujCxf64XKDwa0WytUPwc8r4EGOeOGkuXW%2F4ADphxtvIUyFLLp6A0A%2B3bUWvHE91Ueth%2F%2FA8V669Az%2BcSPN4Xl0Sfe5ESkhxJwG4dBIOowWJrR%2BFGtoUpq%2FTgbxvmR1azV4Z2qXuKDVlEm1PMTMQ6w0YpgWOhf7VSq6EeHMqv9aogE4KX8Aey2NIBVcgMPjsXSPnOXRH0im%2Bq80XPF75N5s%2Fle1Q%2BHY5wHvWIgaAUDyQbrh8e8cM%2BZHhpkMy0UDt6YckW1a4GP8eNCeB3BVWg%3D%3D" -O train.zip
!wget -c "https://storage.googleapis.com/kaggle-competitions-data/kaggle/5441/test.zip?GoogleAccessId=web-data@kaggle-161607.iam.gserviceaccount.com&Expires=1529407229&Signature=AJoqcxiDSiRAlhI%2BdAoWQ6M86nGqTDyHLvfp%2BB2yWai1SnI4%2BBq3wFzYP%2BZQynGLbexUpZqy9LR8UixN3%2F1ZRrQu9d07Tv6f65WAr6w1fifzk%2BW39csagz1NCP1GHN6jqZS95tNmdXJCXXNjGaAI3IzggQr3L6OxdonF%2B%2B2FZjQWOy72jh8Vioi%2BYW%2FGLrAEjy2x5f2ueBvH2LfQBMdChl1pBLcIEwdvUDmIBs629yiFlBKpShiAPb%2BoNEkcBNTDKBUWv%2FiC2TWCsNxaHqMflXjRzc%2BjfoZmTWm1PeN2tbuKV3vfGur6oQvZKqp2yPeqReQhThIVr5y07UiXLBDyeg%3D%3D" -O test.zip
!wget -c "https://storage.googleapis.com/kaggle-competitions-data/kaggle/5441/sample_submission.csv?GoogleAccessId=web-data@kaggle-161607.iam.gserviceaccount.com&Expires=1529408376&Signature=lzVwBh%2B7NIDllvrngUveC3QKOct0KVt7i8IKut8rduWjMrILuyfOX9xqHV0vyqXqYiKanaJq5g6BzEhuPNZLRa9tdQFZkvPbD2shK%2BOgQZKWH0szUBUaws3mMdaCFHmqvsU3D%2BfgvFqW0ZP2F%2Bvu%2B9NARFt6xMom5Ku8LmsiOGvVZ8Hk3ZKDzZ0kR%2FBhZqShoDCY8Heht49GNsCKtU%2FR1i93ILBKCUluWO%2Fa1XB7%2F9X%2FHGlVh4RTu9yOoviSuq0x5zH%2BdEgfcLlrDzSj2Ch%2B16oLZDDfzQlAKhxy4M%2F3%2FGj7kuYGkTAY4a8gRUjoAttWxb7C7QbGRchTmgEkrwLGQA%3D%3D" -O DATASET/submission/sample_submission.csv

# 对数据集解压缩
!unzip train.zip -d DATASET/
!unzip test.zip -d DATASET/

--2018-06-19 05:05:39--  https://storage.googleapis.com/kaggle-competitions-data/kaggle/5441/train.zip?GoogleAccessId=web-data@kaggle-161607.iam.gserviceaccount.com&Expires=1529407270&Signature=TTmj2gZCjWT%2FjzsB1hBLfYURi6aN6LKWNDnKYbW4efA8WYi3D2fvUuO0tGIPtGh5SjujCxf64XKDwa0WytUPwc8r4EGOeOGkuXW%2F4ADphxtvIUyFLLp6A0A%2B3bUWvHE91Ueth%2F%2FA8V669Az%2BcSPN4Xl0Sfe5ESkhxJwG4dBIOowWJrR%2BFGtoUpq%2FTgbxvmR1azV4Z2qXuKDVlEm1PMTMQ6w0YpgWOhf7VSq6EeHMqv9aogE4KX8Aey2NIBVcgMPjsXSPnOXRH0im%2Bq80XPF75N5s%2Fle1Q%2BHY5wHvWIgaAUDyQbrh8e8cM%2BZHhpkMy0UDt6YckW1a4GP8eNCeB3BVWg%3D%3D
Resolving storage.googleapis.com (storage.googleapis.com)... 173.194.202.128, 2607:f8b0:400e:c08::80
Connecting to storage.googleapis.com (storage.googleapis.com)|173.194.202.128|:443... connected.
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Saving to: ‘train.zip’

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2018-06-19 05:05:44 (103 MB/s) - ‘train.zip’ saved [569918665/569918665]

--2018-06-19 05:05:44--  https://storage.googleapis.com/kaggle-competitions-data/kaggle/5441/test.zip?GoogleAccessId=web-data@kaggle-161607.iam.gserviceaccount.com&Expires=1529407229&Signature=AJoqcxiDSiRAlhI%2BdAoWQ6M86nGqTDyHLvfp%2BB2yWai1SnI4%2BBq3wFzYP%2BZQynGLbexUpZqy9LR8UixN3%2F1ZRrQu9d07Tv6f65WAr6w1fifzk%2BW39csagz1NCP1GHN6jqZS95tNmdXJCXXNjGaAI3IzggQr3L6OxdonF%2B%2B2FZjQWOy72jh8Vioi%2BYW%2FGLrAEjy2x5f2ueBvH2LfQBMdChl1pBLcIEwdvUDmIBs629yiFlBKpShiAPb%2BoNEkcBNTDKBUWv%2FiC2TWCsNxaHqMflXjRzc%2BjfoZmTWm1PeN2tbuKV3vfGur6oQvZKqp2yPeqReQhThIVr5y07UiXLBDyeg%3D%3D
Resolving storage.googleapis.com (storage.googleapis.com)... 173.194.202.128, 2607:f8b0:400e:c08::80
Connecting to storage.googleapis.com (storage.googleapis.com)|173.194.202.128|:443... connected.
HTTP request sent, awaiting response... 200 OK
Length: 284478493 (271M) [application/zip]
Saving to: ‘test.zip’

test.zip            100%[===================>] 271.30M  90.3MB/s    in 3.0s    

2018-06-19 05:05:48 (90.3 MB/s) - ‘test.zip’ saved [284478493/284478493]

--2018-06-19 05:05:48--  https://storage.googleapis.com/kaggle-competitions-data/kaggle/5441/sample_submission.csv?GoogleAccessId=web-data@kaggle-161607.iam.gserviceaccount.com&Expires=1529408376&Signature=lzVwBh%2B7NIDllvrngUveC3QKOct0KVt7i8IKut8rduWjMrILuyfOX9xqHV0vyqXqYiKanaJq5g6BzEhuPNZLRa9tdQFZkvPbD2shK%2BOgQZKWH0szUBUaws3mMdaCFHmqvsU3D%2BfgvFqW0ZP2F%2Bvu%2B9NARFt6xMom5Ku8LmsiOGvVZ8Hk3ZKDzZ0kR%2FBhZqShoDCY8Heht49GNsCKtU%2FR1i93ILBKCUluWO%2Fa1XB7%2F9X%2FHGlVh4RTu9yOoviSuq0x5zH%2BdEgfcLlrDzSj2Ch%2B16oLZDDfzQlAKhxy4M%2F3%2FGj7kuYGkTAY4a8gRUjoAttWxb7C7QbGRchTmgEkrwLGQA%3D%3D
Resolving storage.googleapis.com (storage.googleapis.com)... 173.194.202.128, 2607:f8b0:400e:c08::80
Connecting to storage.googleapis.com (storage.googleapis.com)|173.194.202.128|:443... connected.
HTTP request sent, awaiting response... 416 Requested range not satisfiable

    The file is already fully retrieved; nothing to do.

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数据准备

---

• 定义train, test，submission文件夹，并下载数据集，train,test解压缩至相应的文件夹

import os
import re
import random
import shutil
from keras.preprocessing import image
import numpy as np
from tqdm import tqdm
from keras.applications import xception
from keras.applications.xception import Xception
from keras.applications.xception import preprocess_input, decode_predictions

from keras.applications import inception_resnet_v2
from keras.applications.inception_resnet_v2 import InceptionResNetV2
from keras.applications.inception_resnet_v2 import preprocess_input, decode_predictions

from keras.applications import densenet
from keras.applications.densenet import DenseNet201
from keras.applications.densenet import preprocess_input, decode_predictions

from keras.preprocessing.image import ImageDataGenerator, array_to_img, img_to_array, load_img
from keras.models import Sequential, Model
from keras.layers import Conv2D, MaxPooling2D, GlobalAveragePooling2D, Lambda
from keras.layers import Activation, Dropout, Flatten, Dense, Input, Dropout, BatchNormalization
from keras import backend as K
from keras import optimizers
from keras.callbacks import ModelCheckpoint
from sklearn.utils import shuffle
from sklearn.model_selection import train_test_split
import csv
import cv2
import h5py
import matplotlib.image as mpimg
import matplotlib.pyplot as plt
from PIL import Image
%matplotlib inline

/home/ubuntu/anaconda3/envs/tensorflow_p36/lib/python3.6/site-packages/h5py/__init__.py:36: FutureWarning: Conversion of the second argument of issubdtype from `float` to `np.floating` is deprecated. In future, it will be treated as `np.float64 == np.dtype(float).type`.
  from ._conv import register_converters as _register_converters
Using TensorFlow backend.

• 定义文件夹并生成相应的目录

# 初始化数据集相关的所有的文件夹，包含生成的结果集
'''
数据集文件夹结构：
DATASET/
        train/
                dogs/
                cats/
         test/
                pics/
    exception/
   submission/
        
        
'''
# 定义随机数种子，使得每次生成的随机数相同
np.random.seed(1024)

# 定义文件夹
DATASET = "DATASET"
train_dir = DATASET + '/' + 'train'
test_dir = DATASET + '/' + 'test'
submission_dir = DATASET + '/' + 'submission'
exception_dir = DATASET + '/' + 'exception'

def check_folders(dataset_dir, train_dir, test_dir, submis_dir, exception_dir):
    if not os.path.exists(dataset_dir):
        os.makedirs(dataset_dir)
        print("dataset folder is created successfully...")
    else:
        print("dataset folder already created, please check!")
        
    if not os.path.exists(dataset_dir + "/" + train_dir):
        os.makedirs(dataset_dir + "/" + train_dir)
        print("train folder is created successfully...")
    else:
        print("train folder already created, please check!")    
    
    if not os.path.exists(dataset_dir + "/" + test_dir):
        os.makedirs(dataset_dir + "/" + test_dir)
        print("test folder is created successfully...")
    else:
        print("test folder already created, please check!")
        
    if not os.path.exists(dataset_dir + "/" + submis_dir):
        os.makedirs(dataset_dir + "/" + submis_dir)
        print("submission folder is created successfully...")
    else:
        print("submission folder already created, please check!")    
    
    if not os.path.exists(dataset_dir + "/"+ exception_dir):
        os.makedirs(dataset_dir + "/"+ exception_dir)
        print("exception folder is created successfully...")
    else:
        print("exception folder already created, please check!")

check_folders('DATASET', 'train', 'test', 'submission', 'exception')

dataset folder already created, please check!
train folder already created, please check!
test folder already created, please check!
submission folder already created, please check!
exception folder is created successfully...

• 整理ImageNet关于图片的分类数据集，下载地址：https://github.com/zxhx/ImageNetClasses/blob/master/ImageNetClasses.csv
• 读取ImageNetClasses.csv文件并存入字典，以备后面使用

# 下载好的文件"ImageNetFullClasses.csv"位于DATASET目录下，需要将其转换为字典格式存储起来
classes_file_path = "DATASET/ImageNetFullClasses.csv"
ImageNet_full_classes_dict = {}
with open(classes_file_path, "r", encoding='UTF-8') as csv_reader:
    reader = csv.reader(csv_reader)
    for line in reader:
        i_key = line[0]
        i_value = line[1]
        ImageNet_full_classes_dict[i_key] = i_value
print(ImageNet_full_classes_dict)

{'n01440764': '鱼', 'n01443537': '鱼', 'n01484850': '鱼', 'n01491361': '鱼', 'n01494475': '鱼', 'n01496331': '鱼', 'n01498041': '鱼', 'n01514668': '鸡', 'n01514859': '鸡', 'n01518878': '鸵鸟', 'n01530575': '鸟', 'n01531178': '鸟', 'n01532829': '鸟', 'n01534433': '鸟', 'n01537544': '鸟', 'n01558993': '鸟', 'n01560419': '鸟', 'n01580077': '鸟', 'n01582220': '鸟', 'n01592084': '鸟', 'n01601694': '鸟', 'n01608432': '鸟', 'n01614925': '鹰', 'n01616318': '鹰', 'n01622779': '猫头鹰', 'n01629819': '壁虎', 'n01630670': '壁虎', 'n01631663': '壁虎', 'n01632458': '壁虎', 'n01632777': '壁虎', 'n01641577': '蛤蟆', 'n01644373': '青蛙', 'n01644900': '青蛙', 'n01664065': '龟', 'n01665541': '龟', 'n01667114': '龟', 'n01667778': '龟', 'n01669191': '龟', 'n01675722': '蜥蜴', 'n01677366': '蜥蜴', 'n01682714': '蜥蜴', 'n01685808': '蜥蜴', 'n01687978': '蜥蜴', 'n01688243': '蜥蜴', 'n01689811': '蜥蜴', 'n01692333': '蜥蜴', 'n01693334': '蜥蜴', 'n01694178': '蜥蜴', 'n01695060': '蜥蜴', 'n01697457': '鳄鱼', 'n01698640': '鳄鱼', 'n01704323': '恐龙', 'n01728572': '蛇', 'n01728920': '蛇', 'n01729322': '蛇', 'n01729977': '蛇', 'n01734418': '蛇', 'n01735189': '蛇', 'n01737021': '蛇', 'n01739381': '蛇', 'n01740131': '蛇', 'n01742172': '蛇', 'n01744401': '蛇', 'n01748264': '蛇', 'n01749939': '蛇', 'n01751748': '蛇', 'n01753488': '蛇', 'n01755581': '蛇', 'n01756291': '蛇', 'n01768244': '化石', 'n01770081': '蜘蛛', 'n01770393': '蝎子', 'n01773157': '蜘蛛', 'n01773549': '蜘蛛', 'n01773797': '蜘蛛', 'n01774384': '蜘蛛', 'n01774750': '蜘蛛', 'n01775062': '蜘蛛', 'n01776313': '蜘蛛', 'n01784675': '蜈蚣', 'n01795545': '鸟', 'n01796340': '鸟', 'n01797886': '鸟', 'n01798484': '鸟', 'n01806143': '孔雀', 'n01806567': '鸟', 'n01807496': '鸟', 'n01817953': '鸟', 'n01818515': '鸟', 'n01819313': '鸟', 'n01820546': '鸟', 'n01824575': '鸟', 'n01828970': '鸟', 'n01829413': '鸟', 'n01833805': '鸟', 'n01843065': '鸟', 'n01843383': '鸟', 'n01847000': '鸭子', 'n01855032': '鹅', 'n01855672': '鹅', 'n01860187': '鹅', 'n01871265': '大象', 'n01872401': '刺猬', 'n01873310': '鸭嘴兽', 'n01877812': '袋鼠', 'n01882714': '考拉', 'n01883070': '土拨鼠', 'n01910747': '水母', 'n01914609': '珊瑚', 'n01917289': '珊瑚', 'n01924916': '海洋生物', 'n01930112': '海蛇', 'n01943899': '海螺', 'n01944390': '蜗牛', 'n01945685': '蜗牛', 'n01950731': '海洋生物', 'n01955084': '海洋生物', 'n01968897': '海螺', 'n01978287': '螃蟹', 'n01978455': '螃蟹', 'n01980166': '螃蟹', 'n01981276': '螃蟹', 'n01983481': '龙虾', 'n01984695': '龙虾', 'n01985128': '龙虾', 'n01986214': '寄居蟹', 'n01990800': '海洋生物', 'n02002556': '鸟', 'n02002724': '鸟', 'n02006656': '鸟', 'n02007558': '鸟', 'n02009229': '鸟', 'n02009912': '鸟', 'n02011460': '鸟', 'n02012849': '鸟', 'n02013706': '鸟', 'n02017213': '鸟', 'n02018207': '鸟', 'n02018795': '鸟', 'n02025239': '鸟', 'n02027492': '鸟', 'n02028035': '鸟', 'n02033041': '鸟', 'n02037110': '鸟', 'n02051845': '鸟', 'n02056570': '企鹅', 'n02058221': '鸟', 'n02066245': '鲸鱼', 'n02071294': '鲸鱼', 'n02074367': '海象', 'n02077923': '海狮', 'n02085620': '狗', 'n02085782': '狗', 'n02085936': '狗', 'n02086079': '狗', 'n02086240': '狗', 'n02086646': '狗', 'n02086910': '狗', 'n02087046': '狗', 'n02087394': '狗', 'n02088094': '狗', 'n02088238': '狗', 'n02088364': '狗', 'n02088466': '狗', 'n02088632': '狗', 'n02089078': '狗', 'n02089867': '狗', 'n02089973': '狗', 'n02090379': '狗', 'n02090622': '狗', 'n02090721': '狗', 'n02091032': '狗', 'n02091134': '狗', 'n02091244': '狗', 'n02091467': '狗', 'n02091635': '狗', 'n02091831': '狗', 'n02092002': '狗', 'n02092339': '狗', 'n02093256': '狗', 'n02093428': '狗', 'n02093647': '狗', 'n02093754': '狗', 'n02093859': '狗', 'n02093991': '狗', 'n02094114': '狗', 'n02094258': '狗', 'n02094433': '狗', 'n02095314': '狗', 'n02095570': '狗', 'n02095889': '狗', 'n02096051': '狗', 'n02096177': '狗', 'n02096294': '狗', 'n02096437': '狗', 'n02096585': '狗', 'n02097047': '狗', 'n02097130': '狗', 'n02097209': '狗', 'n02097298': '狗', 'n02097474': '狗', 'n02097658': '狗', 'n02098105': '狗', 'n02098286': '狗', 'n02098413': '狗', 'n02099267': '狗', 'n02099429': '狗', 'n02099601': '狗', 'n02099712': '狗', 'n02099849': '狗', 'n02100236': '狗', 'n02100583': '狗', 'n02100735': '狗', 'n02100877': '狗', 'n02101006': '狗', 'n02101388': '狗', 'n02101556': '狗', 'n02102040': '狗', 'n02102177': '狗', 'n02102318': '狗', 'n02102480': '狗', 'n02102973': '狗', 'n02104029': '狗', 'n02104365': '狗', 'n02105056': '狗', 'n02105162': '狗', 'n02105251': '狗', 'n02105412': '狗', 'n02105505': '狗', 'n02105641': '狗', 'n02105855': '狗', 'n02106030': '狗', 'n02106166': '狗', 'n02106382': '狗', 'n02106550': '狗', 'n02106662': '狗', 'n02107142': '狗', 'n02107312': '狗', 'n02107574': '狗', 'n02107683': '狗', 'n02107908': '狗', 'n02108000': '狗', 'n02108089': '狗', 'n02108422': '狗', 'n02108551': '狗', 'n02108915': '狗', 'n02109047': '狗', 'n02109525': '狗', 'n02109961': '狗', 'n02110063': '狗', 'n02110185': '狗', 'n02110341': '狗', 'n02110627': '狗', 'n02110806': '狗', 'n02110958': '狗', 'n02111129': '狗', 'n02111277': '狗', 'n02111500': '狗', 'n02111889': '狗', 'n02112018': '狗', 'n02112137': '狗', 'n02112350': '狗', 'n02112706': '狗', 'n02113023': '狗', 'n02113186': '狗', 'n02113624': '狗', 'n02113712': '狗', 'n02113799': '狗', 'n02113978': '狗', 'n02114367': '狼', 'n02114548': '狼', 'n02114712': '狼', 'n02114855': '狼', 'n02115641': '狼', 'n02115913': '狼', 'n02116738': '狼', 'n02117135': '狼', 'n02119022': '狐狸', 'n02119789': '狐狸', 'n02120079': '狐狸', 'n02120505': '狐狸', 'n02123045': '猫', 'n02123159': '猫', 'n02123394': '猫', 'n02123597': '猫', 'n02124075': '猫', 'n02125311': '猫', 'n02127052': '猫', 'n02128385': '豹', 'n02128757': '豹', 'n02128925': '豹', 'n02129165': '狮子', 'n02129604': '老虎', 'n02130308': '豹', 'n02132136': '熊', 'n02133161': '熊', 'n02134084': '熊', 'n02134418': '熊', 'n02137549': '猫鼬', 'n02138441': '猫鼬', 'n02165105': '昆虫', 'n02165456': '昆虫', 'n02167151': '昆虫', 'n02168699': '昆虫', 'n02169497': '昆虫', 'n02172182': '昆虫', 'n02174001': '昆虫', 'n02177972': '昆虫', 'n02190166': '昆虫', 'n02206856': '昆虫', 'n02219486': '昆虫', 'n02226429': '昆虫', 'n02229544': '昆虫', 'n02231487': '昆虫', 'n02233338': '昆虫', 'n02236044': '昆虫', 'n02256656': '昆虫', 'n02259212': '昆虫', 'n02264363': '昆虫', 'n02268443': '蜻蜓', 'n02268853': '蜻蜓', 'n02276258': '蝴蝶', 'n02277742': '蝴蝶', 'n02279972': '蝴蝶', 'n02280649': '蝴蝶', 'n02281406': '蝴蝶', 'n02281787': '蝴蝶', 'n02317335': '海星', 'n02319095': '海胆', 'n02321529': '海洋生物', 'n02325366': '兔子', 'n02326432': '兔子', 'n02328150': '兔子', 'n02342885': '鼠', 'n02346627': '鼠', 'n02356798': '松鼠', 'n02361337': '鼠', 'n02363005': '鼠', 'n02364673': '鼠', 'n02389026': '马', 'n02391049': '斑马', 'n02395406': '猪', 'n02396427': '猪', 'n02397096': '猪', 'n02398521': '河马', 'n02403003': '牛', 'n02408429': '牛', 'n02410509': '牛', 'n02412080': '羊', 'n02415577': '羊', 'n02417914': '羊', 'n02422106': '羊', 'n02422699': '羊', 'n02423022': '羊', 'n02437312': '骆驼', 'n02437616': '羊驼', 'n02441942': '狸', 'n02442845': '狸', 'n02443114': '狸', 'n02443484': '狸', 'n02444819': '狸', 'n02445715': '狸', 'n02447366': '狸', 'n02454379': '穿山甲', 'n02457408': '树懒', 'n02480495': '狒狒', 'n02480855': '猩猩', 'n02481823': '猴子', 'n02483362': '猴子', 'n02483708': '猴子', 'n02484975': '猴子', 'n02486261': '猴子', 'n02486410': '猴子', 'n02487347': '猴子', 'n02488291': '猴子', 'n02488702': '猴子', 'n02489166': '猴子', 'n02490219': '猴子', 'n02492035': '猴子', 'n02492660': '猴子', 'n02493509': '猴子', 'n02493793': '猴子', 'n02494079': '猴子', 'n02497673': '猴子', 'n02500267': '猴子', 'n02504013': '大象', 'n02504458': '大象', 'n02509815': '浣熊', 'n02510455': '熊猫', 'n02514041': '鱼', 'n02526121': '鱼', 'n02536864': '鱼', 'n02606052': '鱼', 'n02607072': '鱼', 'n02640242': '鱼', 'n02641379': '鱼', 'n02643566': '鱼', 'n02655020': '鱼', 'n02666196': '算盘', 'n02667093': '穆斯林', 'n02669723': '学士服', 'n02672831': '手风琴', 'n02676566': '吉他', 'n02687172': '航空母舰', 'n02690373': '飞机', 'n02692877': '飞艇', 'n02699494': '教堂', 'n02701002': '救护车', 'n02704792': '水陆两用车', 'n02708093': '钟', 'n02727426': '箱子', 'n02730930': '围裙', 'n02747177': '垃圾箱', 'n02749479': '枪', 'n02769748': '背包', 'n02776631': '面包柜', 'n02777292': '体操', 'n02782093': '热气球', 'n02783161': '钢笔', 'n02786058': '邦迪', 'n02787622': '乐器', 'n02788148': '楼梯', 'n02790996': '杠铃', 'n02791124': '座椅', 'n02791270': '理发', 'n02793495': '木屋', 'n02794156': '表', 'n02795169': '酒桶', 'n02797295': '手推车', 'n02799071': '棒球', 'n02802426': '篮球', 'n02804414': '婴儿', 'n02804610': '乐器', 'n02807133': '游泳', 'n02808304': '婴儿毛巾', 'n02808440': '浴缸', 'n02814533': '轿车', 'n02814860': '灯塔', 'n02815834': '烧杯', 'n02817516': '守卫', 'n02823428': '啤酒', 'n02823750': '啤酒', 'n02825657': '建筑', 'n02834397': '围兜', 'n02835271': '双人自行车', 'n02837789': '比基尼', 'n02840245': '笔记本', 'n02841315': '望远镜', 'n02843684': '信箱', 'n02859443': '小屋', 'n02860847': '雪橇', 'n02865351': '项链', 'n02869837': '帽子', 'n02870880': '书柜', 'n02871525': '书店', 'n02877765': '瓶盖', 'n02879718': '弓箭', 'n02883205': '领结', 'n02892201': '墓碑', 'n02892767': '胸罩', 'n02894605': '海岸', 'n02895154': '盔甲', 'n02906734': '扫帚', 'n02909870': '水桶', 'n02910353': '皮带', 'n02916936': '防弹背心', 'n02917067': '火车', 'n02927161': '肉铺', 'n02930766': '出租车', 'n02939185': '锅', 'n02948072': '蜡烛', 'n02950826': '炮', 'n02951358': '艇', 'n02951585': '订书机', 'n02963159': '毛衣', 'n02965783': '反光镜', 'n02966193': '旋转木马', 'n02966687': '工具箱', 'n02971356': '盒子', 'n02974003': '轮胎', 'n02977058': '取款机', 'n02978881': '磁带', 'n02979186': '磁带', 'n02980441': '城堡', 'n02981792': '帆船', 'n02988304': 'cd播放器', 'n02992211': '大提琴', 'n02992529': '手机', 'n02999410': '铁链', 'n03000134': '铁丝网', 'n03000247': '铁丝网', 'n03000684': '电锯', 'n03014705': '木箱', 'n03016953': '木柜', 'n03017168': '锣鼓', 'n03018349': '柜子', 'n03026506': '袜子', 'n03028079': '教堂', 'n03032252': '剧院', 'n03041632': '刀', 'n03042490': '堡垒', 'n03045698': '斗篷', 'n03047690': '鞋子', 'n03062245': '瓶子', 'n03063599': '杯子', 'n03063689': '壶', 'n03065424': '螺旋', 'n03075370': '锁', 'n03085013': '键盘', 'n03089624': '糖果', 'n03095699': '船', 'n03100240': '轿车', 'n03109150': '开瓶器', 'n03110669': '号', 'n03124043': '靴子', 'n03124170': '帽子', 'n03125729': '婴儿床', 'n03126707': '起重机', 'n03127747': '头盔', 'n03127925': '木箱', 'n03131574': '婴儿床', 'n03133878': '电饭锅', 'n03134739': '推球', 'n03141823': '拐杖', 'n03146219': '盔甲', 'n03160309': '水库', 'n03179701': '办公桌', 'n03180011': '电脑', 'n03187595': '电话机', 'n03188531': '尿布', 'n03196217': '闹钟', 'n03197337': '手表', 'n03201208': '餐桌', 'n03207743': '垫子', 'n03207941': '洗碗柜', 'n03208938': '车轮', 'n03216828': '港口', 'n03218198': '雪橇', 'n03220513': '穹顶', 'n03223299': '地毯', 'n03240683': '油田', 'n03249569': '鼓', 'n03250847': '鼓棒', 'n03255030': '哑铃', 'n03259280': '锅', 'n03271574': '风扇', 'n03272010': '电吉他', 'n03272562': '火车', 'n03290653': '电视机', 'n03291819': '信', 'n03297495': '咖啡机', 'n03314780': '化妆品', 'n03325584': '绒毛', 'n03337140': '柜子', 'n03344393': '喷泉', 'n03345487': '消防车', 'n03347037': '壁炉', 'n03355925': '旗杆', 'n03372029': '笛子', 'n03376595': '座椅', 'n03379051': '橄榄球', 'n03384352': '叉车', 'n03388043': '喷泉', 'n03388183': '钢笔', 'n03388549': '床', 'n03393912': '火车', 'n03394916': '圆号', 'n03400231': '平底锅', 'n03404251': '裘皮', 'n03417042': '卡车', 'n03424325': '面具', 'n03425413': '加油', 'n03443371': '酒杯', 'n03444034': '卡丁车', 'n03445777': '高尔夫', 'n03445924': '高尔夫车', 'n03447447': '小船', 'n03447721': '锣鼓', 'n03450230': '婚纱', 'n03452741': '钢琴', 'n03457902': '大棚', 'n03459775': '轿车', 'n03461385': '菜场', 'n03467068': '断头台', 'n03476684': '发饰', 'n03476991': '发蜡', 'n03478589': '坦克', 'n03481172': '榔头', 'n03482405': '竹筒', 'n03483316': '吹风机', 'n03485407': 'pos机', 'n03485794': '手帕', 'n03492542': '硬盘', 'n03494278': '口风琴', 'n03495258': '竖琴', 'n03496892': '起重机', 'n03498962': '斧头', 'n03527444': '手枪', 'n03529860': '电视机', 'n03530642': '蜂巢', 'n03532672': '钩子', 'n03534580': '裙子', 'n03535780': '体操', 'n03538406': '马车', 'n03544143': '沙漏', 'n03584254': '音乐播放器', 'n03584829': '电熨斗', 'n03590841': '南瓜灯', 'n03594734': '牛仔裤', 'n03594945': '吉普车', 'n03595614': 'T恤', 'n03598930': '拼图', 'n03599486': '黄包车', 'n03602883': '操纵杆', 'n03617480': '和服', 'n03623198': '护具', 'n03627232': '绳结', 'n03630383': '医生', 'n03633091': '勺子', 'n03637318': '灯', 'n03642806': '笔记本电脑', 'n03649909': '割草机', 'n03657121': '镜头盖', 'n03658185': '小刀', 'n03661043': '图书馆', 'n03662601': '救生船', 'n03666591': '打火机', 'n03670208': '加长车', 'n03673027': '轮船', 'n03676483': '口红', 'n03680355': '鞋子', 'n03690938': '护肤品', 'n03691459': '音响', 'n03692522': '放大镜', 'n03697007': '原木', 'n03706229': '指南针', 'n03709823': '包', 'n03710193': '邮箱', 'n03710637': '泳衣', 'n03710721': '泳衣', 'n03717622': '窨井盖', 'n03720891': '手摇铃', 'n03721384': '木琴', 'n03724870': '面具', 'n03729826': '火柴', 'n03733131': '绳子', 'n03733281': '迷宫', 'n03733805': '烧杯', 'n03742115': '冰箱', 'n03743016': '石柱', 'n03759954': '话筒', 'n03761084': '微波炉', 'n03763968': '军人', 'n03764736': '水壶', 'n03769881': '小客车', 'n03770439': '短裙', 'n03770679': '面包车', 'n03773504': '导弹', 'n03775071': '手套', 'n03775546': '碗', 'n03776460': '房车', 'n03777568': '老爷车', 'n03777754': '路由器', 'n03781244': '建筑', 'n03782006': '显示器', 'n03785016': '摩托车', 'n03786901': '砚', 'n03787032': '学士帽', 'n03788195': '建筑', 'n03788365': '蚊帐', 'n03791053': '助动车', 'n03792782': '自行车', 'n03792972': '帐篷', 'n03793489': '键盘鼠标', 'n03794056': '捕鼠夹', 'n03796401': '货车', 'n03803284': '狗嘴套', 'n03804744': '钉子', 'n03814639': '颈托', 'n03814906': '项链', 'n03825788': '奶瓶', 'n03832673': '笔记本电脑', 'n03837869': '建筑', 'n03838899': '黑管', 'n03840681': '埙', 'n03841143': '仪表盘', 'n03843555': '机油滤清器', 'n03854065': '管风琴', 'n03857828': '示波器', 'n03866082': '礼服', 'n03868242': '牛车', 'n03868863': '呼吸器', 'n03871628': '零食', 'n03873416': '划桨', 'n03874293': '水轮', 'n03874599': '锁', 'n03876231': '刷子', 'n03877472': '睡衣', 'n03877845': '建筑', 'n03884397': '乐器', 'n03887697': '纸巾', 'n03888257': '降落伞', 'n03888605': '体操', 'n03891251': '长椅', 'n03891332': '停车缴费器', 'n03895866': '火车', 'n03899768': '院子', 'n03902125': '公用电话', 'n03903868': '柱子', 'n03908618': '文具袋', 'n03908714': '卷笔刀', 'n03916031': '香水', 'n03920288': '培养皿', 'n03924679': '打印机', 'n03929660': '吉他拨片', 'n03929855': '头盔', 'n03930313': '栅栏', 'n03930630': '轿车', 'n03933933': '桥', 'n03935335': '储蓄罐', 'n03937543': '药丸', 'n03938244': '枕头', 'n03942813': '乒乓球', 'n03944341': '风车', 'n03947888': '帆船', 'n03950228': '茶壶', 'n03954731': '刨子', 'n03956157': '建筑', 'n03958227': '塑料袋', 'n03961711': '碗架', 'n03967562': '推土机', 'n03970156': '搋子', 'n03976467': '相机', 'n03976657': '杆子', 'n03977966': '警车', 'n03980874': '披风', 'n03982430': '桌球', 'n03983396': '瓶子', 'n03991062': '盆栽', 'n03992509': '陶艺', 'n03995372': '钻机', 'n03998194': '毯子', 'n04004767': '打印机', 'n04005630': '监狱', 'n04008634': '导弹', 'n04009552': '投影仪', 'n04019541': '冰球', 'n04023962': '拳击', 'n04026417': '手提袋', 'n04033901': '羽毛笔', 'n04033995': '床', 'n04037443': '赛车', 'n04039381': '网球', 'n04040759': '加热器', 'n04041544': '收音机', 'n04044716': '卫星接收器', 'n04049303': '酒桶', 'n04065272': '房车', 'n04067472': '鱼竿', 'n04069434': '相机', 'n04070727': '冰箱', 'n04074963': '遥控器', 'n04081281': '餐厅', 'n04086273': '手枪', 'n04090263': '狙击枪', 'n04099969': '摇椅', 'n04111531': '烤箱', 'n04116512': '橡皮', 'n04118538': '橄榄球', 'n04118776': '尺', 'n04120489': '运动鞋', 'n04125021': '保险箱', 'n04127249': '回形针', 'n04131690': '调料瓶', 'n04133789': '拖鞋', 'n04136333': '长裙', 'n04141076': '萨克斯', 'n04141327': '剑', 'n04141975': '秤', 'n04146614': '校车', 'n04147183': '帆船', 'n04149813': '计分板', 'n04152593': '显示器', 'n04153751': '螺丝', 'n04154565': '螺丝刀', 'n04162706': '安全带', 'n04179913': '缝纫机', 'n04192698': '盾牌', 'n04200800': '鞋店', 'n04201297': '榻榻米', 'n04204238': '购物篮', 'n04204347': '购物车', 'n04208210': '铲子', 'n04209133': '浴帽', 'n04209239': '浴帘', 'n04228054': '滑雪', 'n04229816': '面罩', 'n04235860': '睡袋', 'n04238763': '游标卡尺', 'n04239074': '移门', 'n04243546': '老虎机', 'n04251144': '游泳眼镜', 'n04252077': '滑雪车', 'n04252225': '铲雪车', 'n04254120': '洗手液', 'n04254680': '足球', 'n04254777': '袜子', 'n04258138': '太阳能板', 'n04259630': '帽子', 'n04263257': '碗', 'n04264628': '键盘', 'n04265275': '电热器', 'n04266014': '航天飞船', 'n04270147': '锅铲', 'n04273569': '快艇', 'n04275548': '蜘蛛网', 'n04277352': '毛线', 'n04285008': '运动型轿车', 'n04286575': '探照灯', 'n04296562': '乐队', 'n04310018': '蒸汽机车', 'n04311004': '桥', 'n04311174': '鼓', 'n04317175': '听诊器', 'n04325704': '担架', 'n04326547': '石堆', 'n04328186': '秒表', 'n04330267': '火炉', 'n04332243': '滤网', 'n04335435': '公交车', 'n04336792': '担架', 'n04344873': '沙发', 'n04346328': '皇宫', 'n04347754': '轮船', 'n04350905': '西装', 'n04355338': '日晷', 'n04355933': '墨镜', 'n04356056': '墨镜', 'n04357314': '防晒霜', 'n04366367': '桥', 'n04367480': '拖把', 'n04370456': '连帽衫', 'n04371430': '沙滩裤', 'n04371774': '秋千', 'n04372370': '开关', 'n04376876': '针筒', 'n04380533': '台灯', 'n04389033': '坦克', 'n04392985': '磁带播放器', 'n04398044': '茶壶', 'n04399382': '毛绒玩具', 'n04404412': '电视机', 'n04409515': '网球', 'n04417672': '草屋', 'n04418357': '幕布', 'n04423845': '指套', 'n04428191': '装甲车', 'n04429376': '皇位', 'n04435653': '瓦片', 'n04442312': '面包机', 'n04443257': '烟酒店', 'n04447861': '马桶', 'n04456115': '火炬', 'n04458633': '图腾', 'n04461696': '大卡车', 'n04462240': '玩具店', 'n04465501': '拖拉机', 'n04467665': '大货车', 'n04476259': '碟子', 'n04479046': '风衣', 'n04482393': '儿童自行车', 'n04483307': '船', 'n04485082': '三脚架', 'n04486054': '拱门', 'n04487081': '巴士', 'n04487394': '长号', 'n04493381': '浴缸', 'n04501370': '闸机', 'n04505470': '打字机', 'n04507155': '伞', 'n04509417': '独轮车', 'n04515003': '钢琴', 'n04517823': '吸尘器', 'n04522168': '花瓶', 'n04523525': '拱廊', 'n04525038': '珊瑚绒', 'n04525305': '自动贩卖机', 'n04532106': '教皇袍', 'n04532670': '桥', 'n04536866': '小提琴', 'n04540053': '排球', 'n04542943': '煎饼锅', 'n04548280': '挂钟', 'n04548362': '钱夹', 'n04550184': '柜子', 'n04552348': '飞机', 'n04553703': '台盆', 'n04554684': '洗衣机', 'n04557648': '水瓶', 'n04560804': '水壶', 'n04562935': '煤气包', 'n04579145': '水壶', 'n04579432': '哨子', 'n04584207': '头发', 'n04589890': '窗户', 'n04590129': '百叶窗', 'n04591157': '领带', 'n04591713': '葡萄酒', 'n04592741': '飞机', 'n04596742': '炒锅', 'n04597913': '勺子', 'n04599235': '围巾', 'n04604644': '栅栏', 'n04606251': '沉船', 'n04612504': '帆船', 'n04613696': '蒙古包', 'n06359193': '网页', 'n06596364': '海报', 'n06785654': '填字游戏', 'n06794110': '交通标志', 'n06874185': '交通灯', 'n07248320': '书', 'n07565083': '菜单', 'n07579787': '菜', 'n07583066': '菜', 'n07584110': '菜', 'n07590611': '菜', 'n07613480': '蛋糕', 'n07614500': '冰激凌', 'n07615774': '棒冰', 'n07684084': '面包', 'n07693725': '甜甜圈', 'n07695742': '面包', 'n07697313': '汉堡', 'n07697537': '热狗', 'n07711569': '焗饭', 'n07714571': '蔬菜', 'n07714990': '西蓝花', 'n07715103': '花椰菜', 'n07716358': '蔬菜', 'n07716906': '金瓜', 'n07717410': '南瓜', 'n07717556': '南瓜', 'n07718472': '黄瓜', 'n07718747': '蔬菜', 'n07720875': '青椒', 'n07730033': '花', 'n07734744': '蘑菇', 'n07742313': '苹果', 'n07745940': '草莓', 'n07747607': '橙子', 'n07749582': '柠檬', 'n07753113': '水果', 'n07753275': '菠萝', 'n07753592': '香蕉', 'n07754684': '榴莲', 'n07760859': '水果', 'n07768694': '石榴', 'n07802026': '草垛', 'n07831146': '意大利面', 'n07836838': '甜品', 'n07860988': '面团', 'n07871810': '肉酱', 'n07873807': '披萨', 'n07875152': '派', 'n07880968': '肉卷', 'n07892512': '红酒', 'n07920052': '咖啡', 'n07930864': '茶', 'n07932039': '饮料杯', 'n09193705': '雪山', 'n09229709': '泡泡', 'n09246464': '悬崖', 'n09256479': '珊瑚', 'n09288635': '温泉', 'n09332890': '风景', 'n09399592': '小岛', 'n09421951': '沙滩', 'n09428293': '海滩', 'n09468604': '瀑布', 'n09472597': '火山', 'n09835506': '棒球', 'n10148035': '婚礼', 'n10565667': '潜水', 'n11879895': '油菜花', 'n11939491': '菊花', 'n12057211': '植物', 'n12144580': '玉米', 'n12267677': '松果', 'n12620546': '植物', 'n12768682': '栗子', 'n12985857': '菌菇', 'n12998815': '菌菇', 'n13037406': '菌菇', 'n13040303': '菌菇', 'n13044778': '菌菇', 'n13052670': '菌菇', 'n13054560': '菌菇', 'n13133613': '玉米', 'n15075141': '卷筒纸'}

图片集探索

---

• 确认图片集中train与test中的图片数量并随机查看10张图片

# 图片尺寸
img_size = (299,299)
img_size2 = (224,224)
img_shape = (img_size[0], img_size[1], 3)
img_shape2 = (img_size2[0], img_size2[1], 3)
train_path = "DATASET/train" 
#train_path = "train" 
test_path = "DATASET/test"
# 获取指定路径文件名称，返回一个文件名列表
def get_file_names(path):
    files = os.listdir(path)
    return files

# 根据图片路径以及尺寸获取图片数据
def read_img(fp, img_shape):
    img = image.load_img(fp, target_size = img_shape)
    x = image.img_to_array(img)
    x = np.expand_dims(x, axis=0)
    x = preprocess_input(x)
    return(x)

# 获取所有图片的预处理输出，并显示进度
def get_img(file_list, img_shape):    
    return [read_img(i, img_shape) for i in tqdm(file_list)]

file_list = [train_path + "/" + i for i in get_file_names(train_path)]
file_list = [i for i in file_list if os.path.isfile(i)]
test_list = [test_path + "/" + i for i in get_file_names(test_path)]
test_list = [i for i in test_list if os.path.isfile(i)]

# 对file_list按路径中的数字排序
## 定义正则
re_digits = re.compile(r'(\d+)')
## 定义排序函数
def sorted_numbers(s):  
    pieces=re_digits.split(s)  
    pieces[1::2]=map(int,pieces[1::2])      
    return pieces  

def sort_list_with_numbers(alist):  
    return sorted(alist, key=sorted_numbers)

file_list = sort_list_with_numbers(file_list)
# print(get_img(file_list, img_shape))

# 按详细路径显示图片
def show_pic(full_path):
    #img = cv2.resize(mpimg.imread(full_path), (224, 224))
    img = mpimg.imread(full_path)
    plt.imshow(img)
    plt.axis("on")
    plt.title(full_path)
    plt.show()
# print(file_list)

# 确认train和test图片集中文件数量
print("train图片集中图片数量：{}，test图片集中图片数量：{}".format(len(file_list), len(test_list)))

train图片集中图片数量：25000，test图片集中图片数量：12500

# 随机查看train和test中的各5张图片
file_list_sample = random.sample(file_list, 5)
test_list_sample = random.sample(test_list, 5)
show_list = file_list_sample
show_list.extend(test_list_sample)
plt.figure(figsize=(16, 8))
for i in range(len(show_list)):
    plt.subplot(2,5,i+1)
    img = mpimg.imread(show_list[i])
    img = cv2.resize(img, (224, 224))
    plt.axis("on")
    plt.title(show_list[i])
    plt.imshow(img)

# 获取猫和狗的具体类别,输入：字典dic,主要是前面已经获取到的ImageNet_full_classes_dict，输出：只含猫和狗的具体类别code列表
def get_classes(dic):
    cat_dog_classes_code = []
    if dic:
        for k in dic:
            if dic[k] == "猫" or dic[k] == "狗":
                cat_dog_classes_code.append(k)
    else:
        print("ImageNet_full_classes_dict is empty, please check!")
    return cat_dog_classes_code
# print(ImageNet_full_classes_dict)
valid_classes = get_classes(ImageNet_full_classes_dict)
print(valid_classes)

['n02085620', 'n02085782', 'n02085936', 'n02086079', 'n02086240', 'n02086646', 'n02086910', 'n02087046', 'n02087394', 'n02088094', 'n02088238', 'n02088364', 'n02088466', 'n02088632', 'n02089078', 'n02089867', 'n02089973', 'n02090379', 'n02090622', 'n02090721', 'n02091032', 'n02091134', 'n02091244', 'n02091467', 'n02091635', 'n02091831', 'n02092002', 'n02092339', 'n02093256', 'n02093428', 'n02093647', 'n02093754', 'n02093859', 'n02093991', 'n02094114', 'n02094258', 'n02094433', 'n02095314', 'n02095570', 'n02095889', 'n02096051', 'n02096177', 'n02096294', 'n02096437', 'n02096585', 'n02097047', 'n02097130', 'n02097209', 'n02097298', 'n02097474', 'n02097658', 'n02098105', 'n02098286', 'n02098413', 'n02099267', 'n02099429', 'n02099601', 'n02099712', 'n02099849', 'n02100236', 'n02100583', 'n02100735', 'n02100877', 'n02101006', 'n02101388', 'n02101556', 'n02102040', 'n02102177', 'n02102318', 'n02102480', 'n02102973', 'n02104029', 'n02104365', 'n02105056', 'n02105162', 'n02105251', 'n02105412', 'n02105505', 'n02105641', 'n02105855', 'n02106030', 'n02106166', 'n02106382', 'n02106550', 'n02106662', 'n02107142', 'n02107312', 'n02107574', 'n02107683', 'n02107908', 'n02108000', 'n02108089', 'n02108422', 'n02108551', 'n02108915', 'n02109047', 'n02109525', 'n02109961', 'n02110063', 'n02110185', 'n02110341', 'n02110627', 'n02110806', 'n02110958', 'n02111129', 'n02111277', 'n02111500', 'n02111889', 'n02112018', 'n02112137', 'n02112350', 'n02112706', 'n02113023', 'n02113186', 'n02113624', 'n02113712', 'n02113799', 'n02113978', 'n02123045', 'n02123159', 'n02123394', 'n02123597', 'n02124075', 'n02125311', 'n02127052']

清除异常图片：判断异常图片

---

• 主要的可能：非猫非狗、猫狗图像过小或过于模糊感觉无法判断的
• 清除前先显示出来，可以先人工检查并确认一遍，尽量避免误判导致的将有用的特征图片删掉了
• 异常图片转移到'DATASET/exception'文件夹中

# 获取预测结果
def get_preds(model, X, top = 40):
    results = []
    model = model
    for x in tqdm(X):
        pred = model.predict(x)
        pred_decode = decode_predictions(pred, top = top)[0]
        results.append(pred_decode)
    return results

# 使用keras中的预训练模型Xception作为非猫非狗异常图片的筛选器
X = get_img(file_list, img_shape)
X2 = get_img(file_list, img_shape2)

100%|██████████| 25000/25000 [01:36<00:00, 258.47it/s]
100%|██████████| 25000/25000 [01:21<00:00, 306.68it/s]

# model: Xception
model_xception = Xception(weights='imagenet')

# model: DenseNet201
model_DenseNet201 = DenseNet201(weights='imagenet')

# model: InceptionResNetV2
model_InceptionResNetV2 = InceptionResNetV2(weights='imagenet')

# 定义函数:找出非猫非狗的图片，返回except_index，格式：[0, 0, 1, 1, 1]，假设有5张图片，0表示正常，1表示异常
def get_except_index(preds):
    except_index = []
    right_index = []
    right_list_num = []
    for pred in preds:
        right_list_num = [item[0] for item in pred]
        right_list_num = [1 for item in right_list_num if item in valid_classes]
        right_index.append(sum(right_list_num))
    for i in right_index:
        if i == 0:
            except_index.append(1)
        else:
            except_index.append(0)
    return except_index

def exception_info(model, top = 50, shape=img_shape):
    shape_224 = (224,224,3)
    if list(shape)==list(shape_224):
        inpt = X2
    else:
        inpt = X
    preds = get_preds(model, inpt, top = top)
    except_index_name = get_except_index(preds)
    except_index_sum = sum(except_index_name)
    # 异常图片数量
    print("图片总数：{}, top = {}时，异常图片的数量：{}".format(len(except_index_name), top, except_index_sum))
    return except_index_name

# to show exception pics preds when top = 3
except_index_xception_3 = exception_info(model_xception, top = 3)

# to show exception pics preds when top = 10
except_index_xception_10 = exception_info(model_xception, top = 10)

# to show exception pics preds when top = 30
except_index_xception_30 = exception_info(model_xception, top = 30)

# to show exception pics preds when top = 40
except_index_xception_40 = exception_info(model_xception, top = 40)

100%|██████████| 25000/25000 [04:47<00:00, 87.05it/s]

图片总数：25000, top = 40时，异常图片的数量：85

# to show exception pics preds when top = 50
except_index_xception_50 = exception_info(model_xception, top = 50)

# 观察top为不同数值时对应的异常图片数量，为了不漏删，同时减少可能误判的数量，计划将top设置为10
# to show exception pics preds when top = 60
except_index_xception_60 = exception_info(model_xception, top = 60)

• 使用InceptionV3筛选top = 40时的异常图片，返回except_index

except_index_DenseNet201_40 = exception_info(model_DenseNet201, top = 40, shape=img_shape2)

100%|██████████| 25000/25000 [14:43<00:00, 28.30it/s]

图片总数：25000, top = 40时，异常图片的数量：33

• 使用InceptionResNetV2筛选top = 40时的异常图片，返回except_index

except_index_InceptionResNetV2_40 = exception_info(model_InceptionResNetV2, top = 40)

100%|██████████| 25000/25000 [15:33<00:00, 26.80it/s]

图片总数：25000, top = 40时，异常图片的数量：64

清除异常图片：结果分析

---

• top取同样的值时，不同的模型预测的结果不一样
• 观察top=3,10,30,40,50,60时异常图片误判的情况，取top=40的能尽量多的包含异常图片且误判较少
• 取以上三种模型预测结果(except_index_InceptionResNetV2_40, except_index_DenseNet201_40, except_index_xception_40)的并集

# 计算异常图片列表exception_total
ex1 = np.array(except_index_xception_40)
ex2 = np.array(except_index_DenseNet201_40)
ex3 = np.array(except_index_InceptionResNetV2_40)
exception_total = ex1 + ex2 + ex3
exception_total = list(exception_total)
# print(exception_total)

# 显示异常图片
except_list = []
for i,v in enumerate(exception_total):
    if v > 0:
        except_list.append(file_list[i])
        show_pic(file_list[i])
print("异常图片数量：{}, 异常图片显示如上,异常文件列表：{}".format(len(except_list), except_list))

异常图片数量：115, 异常图片显示如上,异常文件列表：['DATASET/train/cat.1288.jpg', 'DATASET/train/cat.1503.jpg', 'DATASET/train/cat.2433.jpg', 'DATASET/train/cat.2520.jpg', 'DATASET/train/cat.2663.jpg', 'DATASET/train/cat.2939.jpg', 'DATASET/train/cat.3004.jpg', 'DATASET/train/cat.3112.jpg', 'DATASET/train/cat.3216.jpg', 'DATASET/train/cat.3472.jpg', 'DATASET/train/cat.3658.jpg', 'DATASET/train/cat.3672.jpg', 'DATASET/train/cat.3791.jpg', 'DATASET/train/cat.4232.jpg', 'DATASET/train/cat.4308.jpg', 'DATASET/train/cat.4338.jpg', 'DATASET/train/cat.4688.jpg', 'DATASET/train/cat.4833.jpg', 'DATASET/train/cat.4842.jpg', 'DATASET/train/cat.4857.jpg', 'DATASET/train/cat.5062.jpg', 'DATASET/train/cat.5071.jpg', 'DATASET/train/cat.5351.jpg', 'DATASET/train/cat.5384.jpg', 'DATASET/train/cat.5418.jpg', 'DATASET/train/cat.5456.jpg', 'DATASET/train/cat.5539.jpg', 'DATASET/train/cat.5609.jpg', 'DATASET/train/cat.5615.jpg', 'DATASET/train/cat.5836.jpg', 'DATASET/train/cat.5974.jpg', 'DATASET/train/cat.6345.jpg', 'DATASET/train/cat.6348.jpg', 'DATASET/train/cat.6817.jpg', 'DATASET/train/cat.6835.jpg', 'DATASET/train/cat.7111.jpg', 'DATASET/train/cat.7372.jpg', 'DATASET/train/cat.7377.jpg', 'DATASET/train/cat.7402.jpg', 'DATASET/train/cat.7545.jpg', 'DATASET/train/cat.7564.jpg', 'DATASET/train/cat.7599.jpg', 'DATASET/train/cat.7601.jpg', 'DATASET/train/cat.7671.jpg', 'DATASET/train/cat.7682.jpg', 'DATASET/train/cat.7968.jpg', 'DATASET/train/cat.8088.jpg', 'DATASET/train/cat.8456.jpg', 'DATASET/train/cat.8470.jpg', 'DATASET/train/cat.8921.jpg', 'DATASET/train/cat.8956.jpg', 'DATASET/train/cat.9171.jpg', 'DATASET/train/cat.9351.jpg', 'DATASET/train/cat.9494.jpg', 'DATASET/train/cat.9983.jpg', 'DATASET/train/cat.10029.jpg', 'DATASET/train/cat.10365.jpg', 'DATASET/train/cat.10627.jpg', 'DATASET/train/cat.10636.jpg', 'DATASET/train/cat.10712.jpg', 'DATASET/train/cat.11184.jpg', 'DATASET/train/cat.11194.jpg', 'DATASET/train/cat.11373.jpg', 'DATASET/train/cat.11427.jpg', 'DATASET/train/cat.11579.jpg', 'DATASET/train/cat.11607.jpg', 'DATASET/train/cat.11777.jpg', 'DATASET/train/cat.12105.jpg', 'DATASET/train/cat.12392.jpg', 'DATASET/train/cat.12424.jpg', 'DATASET/train/cat.12476.jpg', 'DATASET/train/dog.201.jpg', 'DATASET/train/dog.806.jpg', 'DATASET/train/dog.1043.jpg', 'DATASET/train/dog.1259.jpg', 'DATASET/train/dog.1308.jpg', 'DATASET/train/dog.1481.jpg', 'DATASET/train/dog.1773.jpg', 'DATASET/train/dog.1835.jpg', 'DATASET/train/dog.1895.jpg', 'DATASET/train/dog.1962.jpg', 'DATASET/train/dog.2339.jpg', 'DATASET/train/dog.2422.jpg', 'DATASET/train/dog.2614.jpg', 'DATASET/train/dog.3162.jpg', 'DATASET/train/dog.3889.jpg', 'DATASET/train/dog.4127.jpg', 'DATASET/train/dog.4367.jpg', 'DATASET/train/dog.4507.jpg', 'DATASET/train/dog.5336.jpg', 'DATASET/train/dog.5604.jpg', 'DATASET/train/dog.6405.jpg', 'DATASET/train/dog.6475.jpg', 'DATASET/train/dog.6725.jpg', 'DATASET/train/dog.6988.jpg', 'DATASET/train/dog.7772.jpg', 'DATASET/train/dog.8736.jpg', 'DATASET/train/dog.8898.jpg', 'DATASET/train/dog.9149.jpg', 'DATASET/train/dog.9188.jpg', 'DATASET/train/dog.9517.jpg', 'DATASET/train/dog.10072.jpg', 'DATASET/train/dog.10123.jpg', 'DATASET/train/dog.10161.jpg', 'DATASET/train/dog.10190.jpg', 'DATASET/train/dog.10237.jpg', 'DATASET/train/dog.10747.jpg', 'DATASET/train/dog.10801.jpg', 'DATASET/train/dog.10842.jpg', 'DATASET/train/dog.11186.jpg', 'DATASET/train/dog.11266.jpg', 'DATASET/train/dog.11299.jpg', 'DATASET/train/dog.12148.jpg', 'DATASET/train/dog.12331.jpg', 'DATASET/train/dog.12376.jpg']

# 将异常图片移至'DATASET/exception'文件夹，存档
if len(except_list) > 0:
    for i in except_list:
        shutil.move(i, exception_dir)

• 将清除异常图片后的训练图片集进行分类：cat和dog

# 生成新的file_list，并作为训练和验证的基准图片库
file_list = [train_path + "/" + i for i in get_file_names(train_path)]
test_list = [test_path + "/" + i for i in get_file_names(test_path)]
np.random.seed(1024)

cat_list = []
dog_list = []
for i in file_list:
    match_cat = re.findall(r'\bcat\.', i)
    match_dog = re.findall(r'\bdog\.', i)
    if match_cat:
        cat_list.append(i)
    elif match_dog:
        dog_list.append(i)
        
print("处理后的总图片数：{}, 其中猫的数量：{},狗的数量：{}".format(len(cat_list)+len(dog_list), len(cat_list), len(dog_list)))

处理后的总图片数：24885, 其中猫的数量：12429,狗的数量：12456

• 在DATASET/train文件夹中再建立dogs和cats文件夹，在DATASET/test中建立pics文件夹

# 分别建立训练集中的dog文件夹、cat文件夹和测试集文件夹pics
train_dog_dir = DATASET + "/train/dogs"
train_cat_dir = DATASET + "/train/cats"
test_pics_dir = DATASET + "/test/pics"

if not os.path.exists(train_dog_dir):
    os.makedirs(train_dog_dir)
if not os.path.exists(train_cat_dir):
    os.makedirs(train_cat_dir)
if not os.path.exists(test_pics_dir):
    os.makedirs(test_pics_dir)

• 将猫、狗数据归类到相应的cats,dogs文件夹，将test下的数据归类到pics文件夹

for i in cat_list:
    shutil.move(i, DATASET + "/train/cats")
for i in dog_list:
    shutil.move(i, DATASET + "/train/dogs")   
for i in test_list:
    shutil.move(i, DATASET + "/test/pics")

搭建模型

---

• 建一个write_gap函数，输入:MODEL,为模型名称；img_size:图片尺寸，分两种：(299,299)和(224,224)；lambda_func:预处理函数
• 根据输入参数搭建模型并将特征向量训练结果导出

def write_gap(MODEL, img_size, lambda_func=None):
    img_shape = (img_size[0], img_size[1], 3)
    input_tensor = Input(img_shape)
    x = input_tensor
    
    if lambda_func:
        x = Lambda(lambda_func)(x)
    
    # 加载图片
    data_gen = ImageDataGenerator()
    X_train_gen = data_gen.flow_from_directory(train_dir, img_size, shuffle = False,batch_size = 16)
    X_test_gen = data_gen.flow_from_directory(test_dir, img_size, shuffle = False,batch_size = 16, classes = None)
    
    base_model = MODEL(input_tensor=x, weights='imagenet', include_top=False)
    model = Model(base_model.input, GlobalAveragePooling2D()(base_model.output))
    # model.summary()
    # 训练特征向量
    train = model.predict_generator(X_train_gen, verbose = 1)
    test = model.predict_generator(X_test_gen, verbose = 1)
    
    # 保存特征向量
    with h5py.File('saved_models/weights.{}.hdf5'.format(MODEL.__name__)) as h:
        h.create_dataset('train', data = train)
        h.create_dataset('test', data = test)
        h.create_dataset('label', data = X_train_gen.classes)

• 导出特征向量：需要导出特征向量的模型：Xception，InceptionResNetV2，DenseNet201

write_gap(Xception, (299,299), xception.preprocess_input)

Found 24885 images belonging to 2 classes.
Found 12500 images belonging to 1 classes.
1556/1556 [==============================] - 99s 64ms/step
782/782 [==============================] - 48s 61ms/step

write_gap(InceptionResNetV2, (299, 299), inception_resnet_v2.preprocess_input)

Found 24885 images belonging to 2 classes.
Found 12500 images belonging to 1 classes.
1556/1556 [==============================] - 145s 93ms/step
782/782 [==============================] - 70s 89ms/step

write_gap(DenseNet201, (224, 224), densenet.preprocess_input)

Found 24885 images belonging to 2 classes.
Found 12500 images belonging to 1 classes.
1556/1556 [==============================] - 120s 77ms/step
782/782 [==============================] - 56s 71ms/step

• 载入特征向量:
  I) 经过上面的处理，一共获得了3个特征向量文件：
  1) weights.DenseNet201.hdf5
  2) weights.InceptionResNetV2.hdf5
  3) weights.Xception.hdf5
  II) 下面是载入这些特征向量文件并将其合成一个特征向量空间

X_train = []
X_test = []
weights_list = ["saved_models/weights.DenseNet201.hdf5", "saved_models/weights.InceptionResNetV2.hdf5", "saved_models/weights.Xception.hdf5"]
for w in weights_list:
    with h5py.File(w, 'r') as h:
        X_train.append(np.array(h['train']))
        X_test.append(np.array(h['test']))
        y_train = np.array(h['label'])

X_train = np.concatenate(X_train, axis=1)
X_test = np.concatenate(X_test, axis=1)

# 将X_train, y_train进行混洗，以便后续的validation_split使用，numpy的随机种子使用前面定义的
X_train, y_train = shuffle(X_train, y_train)

• 根据新的特征向量构建模型
• 这里需要调节的超参数：Dropout，范围是0~1,一般取0.1~0.9，针对不同的dropout进行手动尝试，最后选取了dropout=0.85

input_tensor = Input(X_train.shape[1:])
x = Dropout(0.85)(input_tensor)
x = Dense(1, activation='sigmoid')(x)
model = Model(input_tensor, x)

• 编译模型

model.compile(optimizer='adadelta',
              loss='binary_crossentropy',
              metrics=['accuracy'])

• 模型概况

model.summary()

_________________________________________________________________
Layer (type)                 Output Shape              Param #   
=================================================================
input_8 (InputLayer)         (None, 5504)              0         
_________________________________________________________________
dropout_1 (Dropout)          (None, 5504)              0         
_________________________________________________________________
dense_1 (Dense)              (None, 1)                 5505      
=================================================================
Total params: 5,505
Trainable params: 5,505
Non-trainable params: 0
_________________________________________________________________

• 模型可视化

from graphviz import Source

src = Source('digraph G {\
    node [shape=record]\
    a[label="DenseNet201|{input:|output:}|{(224, 224, 3)|(2048)}"]\
    b[label="InceptionResNetV2|{input:|output:}|{(299, 299, 3)|(2048)}"]\
    c[label="Xception|{input:|output:}|{(299, 299, 3)|(2048)}"]\
    Merge[label="Merge|{input:|output:}|{(3, 2048)|(5504)}"]\
    Dropout[label="Dropout|Rate:|0.5"]\
    Dense[label="Dense|{input:|output:}|{(5504)|(1024)}"]\
    Output[label="Output|{input:|output:}|{(1024)|(1)}"]\
    Image -> a -> Merge\
    Image -> b -> Merge\
    Image -> c -> Merge\
    Merge -> Dense -> Dropout -> Output\
}')
src

训练模型

---

• 训练模型并保存最佳模型参数

# 定义超参数
epochs = 10
batch_size = 16

# 训练模型并保存模型
checkpointer = ModelCheckpoint(filepath='saved_models/weights.final_model.hdf5',verbose=2, save_best_only=True)
his_model = model.fit(X_train, y_train, validation_split = 0.2,
                                   epochs = epochs, batch_size = batch_size, verbose=2,
                                   callbacks=[checkpointer])

Train on 19908 samples, validate on 4977 samples
Epoch 1/10
 - 22s - loss: 0.0527 - acc: 0.9825 - val_loss: 0.0138 - val_acc: 0.9962

Epoch 00001: val_loss improved from inf to 0.01376, saving model to saved_models/weights.final_model.hdf5
Epoch 2/10
 - 8s - loss: 0.0243 - acc: 0.9930 - val_loss: 0.0138 - val_acc: 0.9966

Epoch 00002: val_loss did not improve from 0.01376
Epoch 3/10
 - 8s - loss: 0.0227 - acc: 0.9940 - val_loss: 0.0143 - val_acc: 0.9966

Epoch 00003: val_loss did not improve from 0.01376
Epoch 4/10
 - 8s - loss: 0.0221 - acc: 0.9943 - val_loss: 0.0139 - val_acc: 0.9974

Epoch 00004: val_loss did not improve from 0.01376
Epoch 5/10
 - 8s - loss: 0.0214 - acc: 0.9947 - val_loss: 0.0152 - val_acc: 0.9966

Epoch 00005: val_loss did not improve from 0.01376
Epoch 6/10
 - 8s - loss: 0.0224 - acc: 0.9949 - val_loss: 0.0151 - val_acc: 0.9970

Epoch 00006: val_loss did not improve from 0.01376
Epoch 7/10
 - 8s - loss: 0.0207 - acc: 0.9948 - val_loss: 0.0150 - val_acc: 0.9972

Epoch 00007: val_loss did not improve from 0.01376
Epoch 8/10
 - 8s - loss: 0.0202 - acc: 0.9953 - val_loss: 0.0154 - val_acc: 0.9972

Epoch 00008: val_loss did not improve from 0.01376
Epoch 9/10
 - 8s - loss: 0.0201 - acc: 0.9954 - val_loss: 0.0162 - val_acc: 0.9968

Epoch 00009: val_loss did not improve from 0.01376
Epoch 10/10
 - 8s - loss: 0.0247 - acc: 0.9945 - val_loss: 0.0157 - val_acc: 0.9970

Epoch 00010: val_loss did not improve from 0.01376

可视化训练过程

• Epochs-Loss曲线

# 模型loss,val_loss曲线
plt.figure(figsize=(12, 5))
plt.subplot(1, 2, 1)
plt.plot(his_model.history['loss'])
plt.plot(his_model.history['val_loss'])
plt.legend(['loss', 'val_loss'])
plt.xlabel('Epochs')
plt.ylabel('Loss')
plt.show()

• Epochs-Acc曲线

# 模型acc,val_acc曲线
plt.figure(figsize=(12, 5))
plt.subplot(1, 2, 2)
plt.plot(his_model.history['acc'])
plt.plot(his_model.history['val_acc'])
plt.legend(['acc', 'val_acc'])
plt.xlabel('Epochs')
plt.ylabel('Acc')
plt.show()

测试集预测

---

y_test = model.predict(X_test, verbose=1)
y_test = y_test.clip(min = 0.005, max = 0.995)

12500/12500 [==============================] - 8s 628us/step

data_gen = ImageDataGenerator()
X_test_gen = data_gen.flow_from_directory(test_dir, img_size, shuffle = False,batch_size = 16, classes = None)

Found 12500 images belonging to 1 classes.

import pandas as pd
from keras.preprocessing.image import *

# 保存数据到csv文件中
csv_file = submission_dir + "/sample_submission.csv"
df = pd.read_csv(csv_file)
re_digits = re.compile(r'(\d+)')
for i, fname in enumerate(X_test_gen.filenames):
    index = int(fname[fname.rfind('/')+1:fname.rfind('.')])
    df.set_value(index-1, 'label', y_test[i])

df.to_csv(submission_dir + '/pred_result.csv', index=None)
# 显示测试集中前10个预测结果
df.head(10)

/home/ubuntu/anaconda3/envs/tensorflow_p36/lib/python3.6/site-packages/ipykernel/__main__.py:10: FutureWarning: set_value is deprecated and will be removed in a future release. Please use .at[] or .iat[] accessors instead

	id	label
0	1	0.995
1	2	0.995
2	3	0.995
3	4	0.995
4	5	0.005
5	6	0.005
6	7	0.005
7	8	0.005
8	9	0.005
9	10	0.005

# 查看test中的前10张图片
test_list_10 = ["DATASET/test/pics/" + i for i in os.listdir("DATASET/test/pics")][:10]
show_list = test_list_10
plt.figure(figsize=(16, 8))
for i in range(len(show_list)):
    plt.subplot(2,5,i+1)
    img = mpimg.imread(show_list[i])
    img = cv2.resize(img, (224, 224))
    plt.axis("on")
    plt.title(show_list[i])
    plt.imshow(img)